Abstract
A kernel regressor to estimate a six-degree-of-fredoom non linear model of an autonomous underwater vehicle is proposed. Although this estimator assumes that the model coefficients are linear combinations of basis functions, it circumvents the problem of specifying the basis functions by using the kernel trick. The Gaussian radial basis function is the chosen kernel, with the Kernel matrix being regularized by its principal components. The variance of the Gaussian radial basis function and the number of principal components are hyper-parameters to be determined by the minimisation of a final prediction error criterion and using the training data. A simulated autonomous underwater vehicle is proposed was used as case study.

Abstract
Blob features are particularly common in acoustic imagery, as isolated objects (e.g., moorings, mines, rocks) appear as blobs in the acquired images. This work focuses the application of the SIFT, SURF, KAZE and U-SURF feature extraction algorithms for blob feature tracking towards Simultaneous Localization and Mapping applications. We introduce a modified feature extraction and matching pipeline intended to improve feature detection and matching precision, tackling performance deterioration caused by the differences between optical and acoustic imagery. Experimental evaluation was undertaken resorting to datasets collected from a water tank structure.

Abstract
One way to mitigate the high costs of doing science or business at sea is to create technological infrastructures possessing all the skills and resources needed for successful maritime operations, and make those capabilities and skills available to the external entities requiring them. By doing so, the individual economic and scientific agents can be spared the enormous effort of creating and maintaining their own, particular set of equivalent capabilities, thus drastically lowering their initial operating costs. In addition to cost savings, operating based on fully-fledged, shared infrastructures not only allows the use of more advanced scientific equipment and highly skilled personnel, but it also enables the business teams (be it industry or research) to focus on their goals, rather than on equipment, logistics, and support. This paper will describe the TEC4SEA infrastructure, created precisely to operate as described. This infrastructure has been under implementation in the last few years, and has now entered its operational phase. This paper will describe it, present its current portfolio of services, and discuss the most relevant assets and facilities that have been recently acquired, so that the research and industrial communities requiring the use of such assets can fully evaluate their adequacy for their own purposes and projects.

Abstract
This article addresses the 3-D localization of a stand-alone acoustic beacon based on the Principle of Synthetic Baseline using a single receiver on board a surface vehicle. The process only uses the passive reception of an acoustic signal with no explicit synchronization, interaction, or communication with the acoustic beacon. The localization process exploits the transmission of periodic signals without synchronization to a known time reference to estimate the time-of-arrival (ToA) with respect to an absolute time basis provided by the global navigation satellite system (GNSS). We present the development of the acoustic signal acquisition system, the signal processing algorithms, the data processing of times-of-arrival, and an estimator that uses times-of-arrival and the coordinates where they have been collected to obtain the 3-D position of the acoustic beacon. The proposed approach was validated in a real field application on a search for an underwater glider lost in September 2021 near the Portuguese coast.

Abstract
Accurate localization is a critical task in underwater navigation. Typical localization methods use a set of acoustic sensors and beacons to estimate relative position, whose geometric configuration has a significant impact on the localization accuracy. Although there is much effort in the literature to define optimal 2D or 3D sensor placement, the optimal sensor placement in irregular and constrained 3D surfaces, such as autonomous underwater vehicles (AUVs) or other structures, is not exploited for improving localization. Additionally, most applications using AUVs employ commercial acoustic modems or compact arrays, therefore the optimization of the placement of spatially independent sensors is not a considered issue. This article tackles acoustic sensor placement optimization in irregular and constrained 3D surfaces, for inverted ultra-short baseline (USBL) approaches, to improve localization accuracy. The implemented multi-objective memetic algorithm combines an evaluation of the geometric sensor's configuration, using the Cramer-Rao Lower Bound (CRLB), with the incidence angle of the received signal. A case study is presented over a simulated homing and docking scenario to demonstrate the proposed optimization algorithm.

Abstract